1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
|
# Copyright (c) Meta Platforms, Inc. and affiliates
# Owner(s): ["oncall: distributed"]
from typing import Any, Callable, Dict, Optional, Sequence
from unittest import skip
import torch
import torch.utils._pytree as pytree
from torch import Tensor
from torch.distributed._tensor import DeviceMesh, distribute_tensor, DTensor
from torch.distributed._tensor.placement_types import (
Partial,
Placement,
Replicate,
Shard,
)
from torch.testing._internal.common_utils import run_tests
from torch.testing._internal.distributed._tensor.common_dtensor import (
DTensorOpTestBase,
skip_unless_torch_gpu,
)
def no_op():
return None
def deepcopy_convert_to_dtensor(
val: Any,
device_mesh: DeviceMesh,
placements: Sequence[Placement],
) -> Any:
"""
Recursively convert (over Sequence and Dict types) Tensors into DTensors.
:param device_mesh: the DeviceMesh to use.
:param placements: the Placement list to use.
:return: the transformed structure.
"""
def f(x):
if isinstance(x, Tensor) and not isinstance(x, DTensor):
return distribute_tensor(
x,
device_mesh=device_mesh,
placements=placements,
)
return x
return pytree.tree_map(f, [val])[0]
def deepcopy_convert_from_dtensor(val: Any) -> Any:
"""
Recursive convert any DTensor to local Tensor.
:param val: the structure to coerce.
:return: the coerced structure.
"""
def f(x):
if isinstance(x, DTensor):
return x.full_tensor()
return x
return pytree.tree_map(f, [val])[0]
class DistElementwiseOpsTest(DTensorOpTestBase):
def _compare_pairwise_ops(
self,
*,
device_mesh: DeviceMesh,
placements: Sequence[Placement],
op: Callable,
pre_op_fn: Optional[Callable] = None,
args: Sequence[Any] = (),
kwargs: Optional[Dict[str, Any]] = None,
):
if pre_op_fn is None:
pre_op_fn = no_op
if not kwargs:
kwargs = {}
dargs = deepcopy_convert_to_dtensor(
args,
device_mesh=device_mesh,
placements=placements,
)
dkwargs = deepcopy_convert_to_dtensor(
kwargs,
device_mesh=device_mesh,
placements=placements,
)
pre_op_fn()
# run the reference first, in case the call is broken;
# it's better to debug an incorrect call at this point.
reference_result = op(*args, **kwargs)
pre_op_fn()
dist_result = op(*dargs, **dkwargs)
collected_result = deepcopy_convert_from_dtensor(dist_result)
self.assertEqualOnRank(reference_result, collected_result)
# TODO: We need to add CPU tests for ops in the future.
def _run_sharded_elementwise_ops(
self,
*,
device_mesh: DeviceMesh,
placements: Sequence[Placement],
pre_op_fn: Optional[Callable] = None,
input_size: Sequence[int],
op: Callable,
**kwargs,
):
if pre_op_fn is None:
pre_op_fn = no_op
input_tensor = torch.randn(
*input_size,
device=self.device_type,
requires_grad=True,
)
self._compare_pairwise_ops(
device_mesh=device_mesh,
placements=placements,
pre_op_fn=pre_op_fn,
op=op,
args=(input_tensor,),
kwargs=kwargs,
)
def test_partial_add(self):
device_mesh = self.build_device_mesh()
d_1 = DTensor.from_local(torch.rand(2, 2), device_mesh, [Partial()])
d_2 = DTensor.from_local(torch.rand(2, 2), device_mesh, [Partial()])
d_3 = d_1 + d_2
self.assertTrue(d_3._spec.placements[0].is_partial())
def test_partial_mul(self):
device_mesh = self.build_device_mesh()
d_1 = DTensor.from_local(torch.ones(2, 2), device_mesh, [Partial()])
d_2 = DTensor.from_local(torch.ones(2, 2), device_mesh, [Partial()])
d_3 = d_1 * d_2
self.assertTrue(d_3._spec.placements[0].is_replicate())
self.assertEqual(d_3.to_local(), torch.ones(2, 2) * (self.world_size**2))
def test_activations(self):
device_mesh = self.build_device_mesh()
self._run_sharded_elementwise_ops(
device_mesh=device_mesh,
placements=[Shard(0)],
input_size=(8, 5),
op=torch.nn.functional.gelu,
)
self._run_sharded_elementwise_ops(
device_mesh=device_mesh,
placements=[Replicate()],
input_size=(8, 5),
op=torch.nn.functional.gelu,
)
self._run_sharded_elementwise_ops(
device_mesh=device_mesh,
placements=[Shard(1)],
input_size=(3, 12),
op=torch.nn.functional.relu,
)
self._run_sharded_elementwise_ops(
device_mesh=device_mesh,
placements=[Replicate()],
input_size=(8, 5),
op=torch.nn.functional.relu,
)
self._run_sharded_elementwise_ops(
device_mesh=device_mesh,
placements=[Shard(0)],
input_size=(8, 5),
op=torch.sigmoid,
)
self._run_sharded_elementwise_ops(
device_mesh=device_mesh,
placements=[Replicate()],
input_size=(8, 5),
op=torch.sigmoid,
)
@skip("testing RNG based ops is broken: https://github.com/pytorch/tau/issues/494")
def test_dropout(self):
device_mesh = self.build_device_mesh()
def _reset_random_seed():
torch.manual_seed(self.rank + 4)
self._run_sharded_elementwise_ops(
device_mesh=device_mesh,
placements=[Shard(0)],
input_size=(8, 5),
op=torch.nn.functional.dropout,
pre_op_fn=_reset_random_seed,
p=0.4,
training=False,
)
self._run_sharded_elementwise_ops(
device_mesh=device_mesh,
placements=[Shard(1)],
input_size=(3, 14),
op=torch.nn.functional.dropout,
pre_op_fn=_reset_random_seed,
p=0.5,
training=True,
)
@skip_unless_torch_gpu
def test_dropout_backward(self):
device_mesh = self.build_device_mesh()
placements = [Shard(0)]
input_size = (8, 5)
grad_output = torch.rand(
input_size,
device=self.device_type,
requires_grad=True,
)
mask = (
torch.rand(
input_size,
device=self.device_type,
requires_grad=False,
)
< 0.8
)
self._compare_pairwise_ops(
device_mesh=device_mesh,
placements=placements,
op=torch.ops.aten.native_dropout_backward,
kwargs=dict(
grad_output=grad_output,
mask=mask,
scale=0.3,
),
)
def test_dropout_errors(self):
device_mesh = self.build_device_mesh()
with self.assertRaisesRegex(RuntimeError, "supported"):
self._run_sharded_elementwise_ops(
device_mesh=device_mesh,
placements=[Partial("sum")],
input_size=(8, 5),
op=torch.nn.functional.dropout,
)
def test_mul_out(self):
device_mesh = self.build_device_mesh()
torch.manual_seed(self.rank)
shard_spec = [Shard(0)]
input_size = (8, 4)
input_tensor = torch.randn(*input_size, device=self.device_type)
dtensor = DTensor.from_local(input_tensor, device_mesh, shard_spec)
other_tensor = torch.randn(*input_size, device=self.device_type)
other_dtensor = DTensor.from_local(other_tensor, device_mesh, shard_spec)
output_tensor = torch.randn(*input_size, device=self.device_type)
output_dtensor = DTensor.from_local(output_tensor, device_mesh, shard_spec)
dt = torch.mul(dtensor, other_dtensor, out=output_dtensor)
expected = torch.mul(input_tensor, other_tensor, out=output_tensor)
self.assertEqual(input_tensor, dtensor.to_local())
self.assertEqual(expected, dt.to_local())
if __name__ == "__main__":
run_tests()
|
